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Voltar para Mathematics for Machine Learning: PCA

Comentários e feedback de alunos de Mathematics for Machine Learning: PCA da instituição Imperial College London

2,168 classificações
536 avaliações

Sobre o curso

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Melhores avaliações


Jul 17, 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.


Jun 19, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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201 — 225 de 531 Avaliações para o Mathematics for Machine Learning: PCA

por Jyothula S K

May 18, 2020

Very Good Course to Learn about PCA

por Carlos S

Jun 11, 2018

What you need to understand PCA!!!

por Dina B

Aug 08, 2020

Nice course - informative and fun

por saketh b

Aug 10, 2020

The instructor did a great job!

por Sukrut S B

Oct 19, 2020

Try to make it little bit easy

por Israel d S R d A

Jun 05, 2020

Great course very recommended

por Gautham T

Jun 16, 2019

excellent course by imperial

por Ankur A

May 15, 2020

Tough course, learnt a lot.

por imran s

Dec 20, 2018

Great Coverage of the Topic

por Ajay S

Apr 09, 2019

Great course for every one

por Ricardo C V

Dec 25, 2019

Challenging but Excellent


Jul 17, 2020

Excellent course content


Jul 02, 2020

This course is very good

por Pranav N

Aug 25, 2020

Amazing overall course

por Gazi J H

Oct 16, 2020

Thank you very much.

por Yasser Z S E

May 26, 2020

Thank you very match

por wonseok k

Mar 03, 2020

hard but good course

por Keisuke F

Sep 15, 2019

I had big fun of PCA

por Rajkumar R

Jun 20, 2020

I enjoyed learning.

por Omar Y B L

Jul 15, 2020

Cruel pero justo!!

por N'guessan L R G

Apr 15, 2020

Amazing Course!!!!

por Dominik B

Feb 17, 2020

Great instructor!

por Sujeet B

Jul 21, 2019

Tough, but great!

por Jitender S V

Jul 25, 2018


por Shanxue J

May 23, 2018

Truly exceptional